Bibliographic record
Abstract
My thesis research focused on the development and application of analytical methods that enabled arsenic speciation in biological and environmental samples. A set of complementary chromatographic separation techniques were combined with inductively coupled plasma mass spectrometry and hydride generation. These techniques allowed for the separation and detection of arsenobetaine, arsenite, arsenate, monomethylarsonic acid, and dimethylarsinic acid. The application of these techniques to the determination of arsenic species in human urine has contributed to arsenic exposure measurement in a collaborative pilot epidemiological study. The application of a high performance liquid chromatography – inductively coupled plasma mass spectrometry technique showed that most of the groundwater samples from the Battersea Drain watershed located in southern Alberta had arsenic concentrations below the Canadian drinking water guideline value of 10 µg L-1. A set of complementary chromatographic separation techniques coupled with inductively coupled plasma mass spectrometry was developed to characterize a new arsenic species, Arsenicin A, previously reported for the presence in a marine sponge. These techniques enabled the separation and detection of an Arsenicin A model compound, arsenite, arsenate, monomethylarsonic acid, dimethylarsinic acid, arsenobetaine, and an arsenosugar. The application of these techniques to the determination of arsenic species in marine sponges suggested that arsenic speciation profile may be organism and habitat dependent. A comparative cellular uptake study that used human lung carcinoma A549 cells showed that these cells were able to uptake two orders of magnitude more Arsenicin A model compound than arsenite. The higher cellular uptake of Arsenicin A model compound was consistent with the higher toxicity of Arsenicin A model compound as compared to arsenite, suggesting that the cellular uptake is an important factor contributing to the toxicity of these arsenic species. My Ph.D. research has provided analytical techniques that are useful to environmental and biological studies.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".